Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,360
2	South Dakota	124,573
3	Rhode Island	114,050
4	Utah	112,473
5	Arizona	109,372
6	Tennessee	108,624
7	Iowa	104,288
8	Wisconsin	104,191
9	Oklahoma	104,189
10	Arkansas	103,572
11	Nebraska	102,072
12	Kansas	99,271
13	Alabama	97,866
14	Indiana	96,675
15	Mississippi	96,212
16	South Carolina	93,837
17	Idaho	93,766
18	Nevada	93,464
19	Illinois	91,796
20	Wyoming	91,723
21	Montana	91,074
22	Louisiana	90,041
23	Texas	88,437
24	Georgia	88,399
25	California	88,049
26	Kentucky	87,618
27	New Mexico	85,981
28	Delaware	84,899
29	Florida	84,828
30	Minnesota	83,845
31	New Jersey	83,742
32	Missouri	82,808
33	Massachusetts	80,652
34	Ohio	80,206
35	New York	78,741
36	North Carolina	78,449
37	Alaska	76,561
38	Connecticut	74,983
39	Colorado	72,109
40	West Virginia	71,022
41	Pennsylvania	70,070
42	Virginia	64,134
43	Michigan	62,973
44	Maryland	61,252
45	District of Columbia	54,971
46	New Hampshire	51,852
47	Washington	43,471
48	Puerto Rico	40,995
49	Oregon	35,572
50	Maine	31,556
51	Vermont	21,732
52	Hawaii	18,878

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	647
2	New York	470
3	New Jersey	418
4	Delaware	414
5	Virginia	394
6	North Carolina	387
7	Oklahoma	383
8	Texas	370
9	Florida	367
10	Georgia	365
11	Utah	337
12	Massachusetts	336
13	Kentucky	328
14	New Hampshire	308
15	Tennessee	303
16	Rhode Island	291
17	Mississippi	290
18	Arkansas	289
19	Pennsylvania	284
20	Colorado	282
21	Louisiana	279
22	Nebraska	279
23	Arizona	266
24	Alabama	257
25	Ohio	254
26	West Virginia	247
27	California	246
28	New Mexico	238
29	South Dakota	235
30	Vermont	234
31	Indiana	216
32	Nevada	216
33	District of Columbia	211
34	Iowa	208
35	Montana	205
36	Illinois	195
37	Wisconsin	194
38	Maryland	191
39	Connecticut	172
40	Minnesota	170
41	Idaho	169
42	Kansas	148
43	North Dakota	147
44	Missouri	146
45	Michigan	141
46	Alaska	139
47	Maine	132
48	Oregon	123
49	Wyoming	122
50	Washington	121
51	Puerto Rico	85
52	Hawaii	52

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,526
2	New York	2,328
3	Massachusetts	2,237
4	Mississippi	2,170
5	Rhode Island	2,161
6	South Dakota	2,077
7	Connecticut	2,070
8	Arizona	2,054
9	Louisiana	1,995
10	North Dakota	1,917
11	Alabama	1,884
12	Pennsylvania	1,805
13	Indiana	1,804
14	Illinois	1,743
15	Arkansas	1,740
16	New Mexico	1,677
17	Iowa	1,659
18	Michigan	1,612
19	Tennessee	1,583
20	South Carolina	1,536
21	Nevada	1,524
22	Kansas	1,497
23	Georgia	1,448
24	Texas	1,425
25	Ohio	1,397
26	District of Columbia	1,387
27	Florida	1,335
28	Delaware	1,316
29	Missouri	1,282
30	Maryland	1,246
31	Montana	1,242
32	West Virginia	1,228
33	California	1,186
34	Wisconsin	1,158
35	Minnesota	1,141
36	Wyoming	1,117
37	Nebraska	1,097
38	Colorado	1,026
39	Idaho	1,010
40	Oklahoma	1,009
41	North Carolina	999
42	Kentucky	994
43	New Hampshire	831
44	Virginia	819
45	Washington	622
46	Puerto Rico	599
47	Utah	558
48	Oregon	506
49	Maine	481
50	Alaska	370
51	Vermont	302
52	Hawaii	298

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Ohio	127
2	Alabama	24
3	Arizona	20
4	Missouri	14
5	California	12
6	Delaware	12
7	Georgia	12
8	Nevada	11
9	Texas	11
10	Massachusetts	10
11	Mississippi	10
12	South Carolina	10
13	New Jersey	9
14	Rhode Island	9
15	Tennessee	9
16	Arkansas	8
17	New Mexico	8
18	North Carolina	8
19	Pennsylvania	8
20	South Dakota	8
21	Florida	7
22	Indiana	7
23	Kentucky	7
24	New York	7
25	Oklahoma	7
26	District of Columbia	6
27	Iowa	6
28	Kansas	6
29	Oregon	6
30	Connecticut	5
31	Illinois	5
32	Michigan	5
33	Louisiana	4
34	Maryland	4
35	West Virginia	4
36	New Hampshire	3
37	Colorado	2
38	Idaho	2
39	Minnesota	2
40	Montana	2
41	Nebraska	2
42	Utah	2
43	Virginia	2
44	Washington	2
45	Wisconsin	2
46	Maine	1
47	Puerto Rico	1
48	Vermont	1
49	Alaska	0
50	Hawaii	0
51	North Dakota	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	317,934	1	99
Chattahoochee	Georgia	265,242	2	99
Bent	Colorado	254,617	3	99
Lincoln	Arkansas	239,481	4	99
Lake	Tennessee	236,460	5	99
Davidson	Tennessee	126,014	228	92
Richland	South Carolina	92,578	1258	59
York	South Carolina	86,241	1584	49
Orange	California	80,773	1836	41
Pierce	Washington	40,612	2903	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Iron	Wisconsin	6,682	3	99
Buffalo	South Dakota	6,626	4	99
Galax city	Virginia	6,617	5	99
Davidson	Tennessee	1,164	1931	38
Orange	California	1,116	1986	36
Richland	South Carolina	1,114	1990	36
York	South Carolina	1,036	2098	33
Pierce	Washington	560	2682	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.